Abstract

We present a three-dimensional (3D) object tracking method based on a Bayesian framework for tracking multiple, occluded objects in a complex scene. The 3D passive capture of scene data is based on integral imaging. The statistical characteristics of the objects versus the background are exploited to analyze each frame. The algorithm can work with objects with unknown position, rotation, scale, and illumination. Posterior probabilities of the reconstructed scene background and the 3D objects are calculated by defining their pixel intensities as Gaussian and gamma distributions, respectively, and by assuming appropriate prior distributions for estimated parameters. Multiobject tracking is achieved by maximizing the geodesic distance between the log-posteriors of the background and the objects. Experimental results are presented.